ABSTRACT
Background: Percutaneous mechanical thrombectomy is an increasingly utilized treatment modality for acute pulmonary embolism (PE), improving pulmonary flow with embolus/thrombus modification. We aimed to investigated national trends and outcomes in patients with acute pulmonary embolism with and without cor pulmonale undergoing mechanical thrombectomy. Method(s): We utilized the National Inpatient Sample (NIS) Database 2016-2020 to identify the cohort with principal diagnosis of acute pulmonary embolism, with or without cor pulmonale using ICD-10 codes. Patients who had COVID-19 infection during hospital stay were excluded from the 2020 database. Primary outcome analysis included adjusted inpatient mortality rate utilizing predictive margins, during each calendar year stratified by sex, age, race, and median household income (MHOI). Result(s): There were a total of 389,527 hospitalizations (367,205 without cor pulmonale;22,322 with cor pulmonale) with a principal diagnosis of acute PE from 2016-2020. Out of these hospitalizations, 3,168 (0.81%) underwent mechanical thrombectomy during hospital stay. Ratio of mechanical thrombectomy amongst all PEs increased significantly throughout the years (0.39% in 2016 vs 1.68% in 2020, p trend <0.001). There was a significant decline in mortality of patients without cor pulmonale undergoing mechanical thrombectomy (12.72% in 2016 vs. 8.30% in 2020, p-trend <0.001), however this trend was not observed in patients with cor pulmonale (12.11% in 2016 vs. 8.87% in 2020, p-trend = 0.45). Conclusion(s): Our findings suggest that, throughout the years of 2016-2020, there was a trend suggesting an increase in ratio of mechanical thrombectomy amongst patients admitted with pulmonary embolism and decrease in inpatient mortality in patients without cor pulmonale undergoing mechanical thrombectomy. [Formula presented]Copyright © 2023
ABSTRACT
It is crucial that Breast Cancer should be detected early. Breast cancer time series forecasting is a novel data - driven approach to breast cancer diagnosis. Instead of looking at static images of the medical records, it analyses the dynamics in the tumour's growth rate, especially in its early stages. It uses machine learning models to find patterns that are not readily observable in static images, but are predictive of later outcomes. During COVID-19 it is necessary to monitor patient from home and IOT devices can be used that give moment forecast to the client and doctor during their typical day by day routine. Various Machine learning models are reviewed for classification of Breast Cancer symptoms. It is observed that data visualization and feature engineering play a crucial role in the classification before applying any model on data set. For human protection during COVID-19 it is better to depend on IoT enabled wearable device for automatic detection and appointment. The IoT enabled devices can use power of cloud computing and machine learning models to complete the framework of getting treated at home. Security of the data is another aspect to be taken into consideration. Solutions are available for the whole process and their aggregation will result in generating the desired model. In this paper, model is proposed to diagnose breast cancer at home using IoT, Blockchain, Machine learning and Cloud Computing. © 2022 American Institute of Physics Inc.. All rights reserved.
ABSTRACT
Introduction: Due to the COVID-19 pandemic, there may be changes in continuous positive airway pressure (CPAP) adherence. This study aimed to examine the longitudinal effect of using CPAP as advised and self-reported sleep quality improvements in sleep medicine clinic patients using CPAP early in the pandemic and six months later. Methods: Between June-November 2020, 81 sleep medicine clinic patients completed an online survey that included questions about CPAP use, using CPAP as advised, and changes in sleep quality associated with CPAP use. Patients were recontacted 6 months later to complete the same survey. Among survey respondents completing both surveys, 27 (50%;aged 58±18.2 y, 48% female, 67% Caucasian) reported using CPAP and were included in the present analysis. We conducted multivariate regression analyses Chisquare Association tests to determine whether self-reported CPAP use, CPAP use as advised, and sleep quality changed from baseline to 6-month follow up during the pandemic. Results: Among CPAP users, 89% reported no change, 7% reported they use CPAP more, and 4% reported they use CPAP less at 6-month follow up. There was a significant increase in using CPAP as advised at 6-month follow up compared to the baseline survey, p=0.003. Additionally, there was a significant improvement self-reported sleep quality while using CPAP at 6-month follow up compared to the baseline survey, p=0.012. Conclusion: Patients reported an increase in using CPAP advised and improvements in sleep quality as a result of CPAP use at 6-month follow up compared to a baseline survey administered early in the pandemic. Understanding why patients are more adherent to using CPAP as advised during the pandemic may help in developing interventions to increase CPAP adherence.
ABSTRACT
Background: Small studies have reported that high levels of free SARS-CoV-2 nucleocapsid-antigen in sera (N-antigenemia) was associated with prognostic. Here, we assessed the association between N-antigenemia levels and patient's outcome in a large cohort of hospitalized patients. Methods: We analysed data from patients with at least one sera sample that were included in the French COVID cohort between January, 25 2020 and September, 2 2020. N-antigenemia levels were determined with the COV-Quanto® assay (AAZ) with a limit of detection of 2.98 pg/mL. IgG (anti-N, anti-S and anti-RBD) levels were assessed using the V-PLEX panel assay (MSD). Patient's outcome was classified in three groups: death, recovery with ICU transfer (ICU) and recovery without ICU transfer (Hospital). Results: We included 1166 samples from 357 patients, with 66% of male and a median age at 63 years [IQR: 52-71]. A total of 82, 96 and 142 patients were in the Death, ICU and Hospital groups, respectively. The sensitivity of N-antigenemia was 79% (131/165) within the first 10 days SSO (since symptoms onset) and 62% (365/589) from 11 to 30 days SSO. Positivity rates were significantly different across severity groups from 0 to 15 days SSO: 95% (95/100), 64% (118/183), 79% (83/105) (p<0.001) for Death, ICU and Hospital groups, respectively. Among positive patients, a significant gradient was found in the levels of N-antigenemia according to disease severity, with median levels of 302, 134 and 89 in Death, ICU and hospital groups, respectively. Similar relationships were found when stratifying on the time SSO (see figure). Overall, 95, 80, 43 and 22% of N-antigenemia >10,000, >5,000, >1,000 and <1,000 pg/mL corresponded to patients who died. IgG antibodies titers were not correlated to severity and the presence of both sera N-antigen and anti-N IgG was observed for 42% (490/1166) samples. Conclusion: We observed, on a large prospective cohort, a strong relationship between N-antigenemia and COVID-19 severity. This new diagnostic tool should help to prognostic evaluation of COVID-19 patients. To our knowledge, COVID-19 is the first demonstration of the presence of free antigenemia in a viral pneumonia, and its association with prognostic.
ABSTRACT
From the past several year's educational institutions at every level was practicing conventional teaching-learning process. It seems to be the only way of imparting education to the learners. However, due to the onset of Coronavirus, i.e., Covid19, everything came to a halt for a while all over the world. The complete ecosystem of human society gets affected by this. Educational institutions also hit hard by this pandemic, and the complete layout of the teaching-learning process over the globe changed. It brings challenges for both students and teachers to withstand in this tough situation and to restore the backbone of learning. E-learning activities slowly-slowly gripped the complete educational ecosystem and the way of imparting education also changed. The primary purpose of this study is to examine the level of cognition faced by e-learners as compared to traditional learners at higher education level during Covid19. For this study, 120 students from Chitkara University have voluntarily participated and submitted their responses through the Google Form. It deduces from this study that learners using E-learning mode feel more cognitive load as compared to traditional learners. The cohen's d-value which represents the effect size comes to be 0.97 which means that the effect of cognitive load is significantly large on e-learners than the traditional learners. In the future, the reasons behind the rise of the cognition level of e-learners need to evaluate and improve the learning gain of e-learners. © 2022 Author(s).
ABSTRACT
Background: Multisystem inflammatory syndrome in children (MIS-C) has been recognised as a rare and serious complication that involves multiple systems. Gastrointestinal (GI) symptoms like pain abdomen, vomiting, loose stools are common presenting features. Despite an abundance of ACE2 and TMPRSS2 cell receptors in intestine and biliary epithelium severe liver dysfunction is uncommon and very few studies have elaborated hepatic manifestations. Aims: To analyse spectrum of hepatic manifestations in MIS-C. Methods: We retrospectively reviewed the data of children diagnosed with MIS-C at our centre during first and second COVID wave (April 2020-May2021). 30 children were identified and recruited in the study. Their demographic, clinical and biochemical parameters were studied. Results: The mean age of presentation was 7 years. 76.6%(n=23) were male and 23.3%(n=7) were female. 90%(n=27) children had concomitant or isolated gastrointestinal complaints. 44.4% presented with abdominal pain (n=12), 33.3%(n=9) had loose stools, 18.5%(n=5) had vomiting. Only 1 child presented with blood in stool. All patients had positive COVID-19 IgG antibodies (mean titre- 40.1AU/ml). Mean C-reactive protein was 94.7mmHr. 50%(n=15) had deranged liver function tests. Both hyperbilirubinemia with raised liver enzymes were noted in 3(20%), both aspartate aminotransferase (AST) and alanine aminotransferase (ALT) elevation in 8 (53.3%), isolated AST elevation in 4 (26.6%). International normalised ratio (INR) was normal in all. Abdominal imaging (n=8) was normal in 2(25%), two showed distal ileal diffuse mural thickening, two had cholecystitis and one had pancreatitis. 1 expired (3.3%) and 29(96.6%) were discharged successfully. Conclusion: GI manifestations are common and so is the hepatobiliary involvement. Hepatocellular injury leading to hepatitis pattern is common, but involvement of pancreatico-biliary system should also be ruled out. Prognosis is excellent without any residual damage to the liver clinically, biochemically, and radiologically.
ABSTRACT
Background: The coronavirus disease of 2019 (COVID-19) is a global pandemic with over 200 million cases and four million deaths worldwide. Anti-COVID-19 vaccinations have had exceptional success in subduing the incidence, prevalence, and disease severity of COVID-19, but rare cases of myocarditis have been reported after COVID-19 vaccinations. Methods: We performed a systematic literature search on PUBMED, MEDLINE, EMBASE, and Cochrane Reviews database from inception to July 18, 2021. Studies were analyzed based on predetermined eligibility criteria. Results: A total of 19 studies containing 73 cases of COVID-19 vaccine-associated myocarditis were catalogued. Mean age was 25 years, and male to female ratio was 17:1. For 87.7% of patients, myocarditis occurred after the second dose. Average time to onset and length of hospitalization were 3.5 days and 5.2 days, respectively. Prognosis was benign with 100% recovery. Chest pain (100%);elevation of troponin (100%) and CRP (94.4%);and ST elevation on EKG (81.4%) were common. NSAIDs (73.5%) were the most used medication, followed by colchicine (50%). Conclusions: Patients with COVID-19 vaccine-associated myocarditis are usually younger males presenting with chest pain 3.5 days after receiving their second dose. Work-up typically shows elevation of troponin and CRP with ST changes in EKG. Diagnosis is made after excluding all other etiologies. Given significant population benefit from COVID-19 vaccination, physicians should continue to encourage vaccination while remaining vigilant of the very rare occurrence of myocarditis following COVID-19 vaccination. [Formula presented]
ABSTRACT
Purpose: The purpose of this paper is to give an overview of artificial intelligence (AI) and other AI-enabled technologies and to describe how COVID-19 affects various industries such as health care, manufacturing, retail, food services, education, media and entertainment, banking and insurance, travel and tourism. Furthermore, the authors discuss the tactics in which information technology is used to implement business strategies to transform businesses and to incentivise the implementation of these technologies in current or future emergency situations. Design/methodology/approach: The review provides the rapidly growing literature on the use of smart technology during the current COVID-19 pandemic. Findings: The 127 empirical articles the authors have identified suggest that 39 forms of smart technologies have been used, ranging from artificial intelligence to computer vision technology. Eight different industries have been identified that are using these technologies, primarily food services and manufacturing. Further, the authors list 40 generalised types of activities that are involved including providing health services, data analysis and communication. To prevent the spread of illness, robots with artificial intelligence are being used to examine patients and give drugs to them. The online execution of teaching practices and simulators have replaced the classroom mode of teaching due to the epidemic. The AI-based Blue-dot algorithm aids in the detection of early warning indications. The AI model detects a patient in respiratory distress based on face detection, face recognition, facial action unit detection, expression recognition, posture, extremity movement analysis, visitation frequency detection, sound pressure detection and light level detection. The above and various other applications are listed throughout the paper. Research limitations/implications: Research is largely delimited to the area of COVID-19-related studies. Also, bias of selective assessment may be present. In Indian context, advanced technology is yet to be harnessed to its full extent. Also, educational system is yet to be upgraded to add these technologies potential benefits on wider basis. Practical implications: First, leveraging of insights across various industry sectors to battle the global threat, and smart technology is one of the key takeaways in this field. Second, an integrated framework is recommended for policy making in this area. Lastly, the authors recommend that an internet-based repository should be developed, keeping all the ideas, databases, best practices, dashboard and real-time statistical data. Originality/value: As the COVID-19 is a relatively recent phenomenon, such a comprehensive review does not exist in the extant literature to the best of the authors’ knowledge. The review is rapidly emerging literature on smart technology use during the current COVID-19 pandemic. © 2022, Emerald Publishing Limited.
ABSTRACT
The pandemic in 2020 and the resultant global lockdown led to new dynamics in the homestead. Families faced an accelerated process of digitization of conventional processes like education and white collar jobs. Working women with children of school or kindergarten age were forced to redraw the boundaries between the personal and the professional as the entire family essentially functioned from home, which increased prevalent gender inequalities. Tasks like 'planning', care-giving, emotional care etc. go unaccounted for and remain invisible to other members of the household. We present a feminist design research project that explores how design research can contribute to greater social justice and gender equality by a fairer distribution of domestic labour. We conducted semi-structured interviews in India during the pandemic lockdown, to elicit both explicit as well as tacit information on daily workloads of working women with families. We found disparity in domestic duties between genders, with specific regard to the circumstances created by the 2020 pandemic conditions. Based on our findings and analysis, we prototyped a visualization tool that makes invisible domestic work visible. Our research contribution is a design research methodology to generate insights to enable creation of design interventions for social justice. This was achieved through use of established design research methods, that are conventionally used in face-to-face settings, which were then adapted to exceptional pandemic conditions that required social distancing where communication was remote, and usually virtual. © 2021 ACM.
ABSTRACT
Traditional teaching got a blow during lockdown because of pandemic condition and all of a sudden a need for transformation from traditional teaching to technology oriented teaching was realized. The paper aims at elaborating the paradigm shift in engineering education teaching and learning methods using online tools and focuses on evaluating the usability of proposed online learning models by the students. Data is collected through a survey questionnaire responded by 60 students of the engineering of Chitkara University. The findings are limited to only one mode of platform that is gotowebinar so they cannot be generalized beyond this concept. Future research should be considered using all possible platforms, which are available for higher education teaching. Originality- This research explores the determinants of education's acceptance of online mode of education and also the adoption from chalkboards to talk boards. © 2020 IEEE.
ABSTRACT
The education ecosystem in India is primarily dependent on face to face interaction of teachers with students. Technology assistive tools available to educators act as an aid for imparting quality education. However, the complete transition from face-to-face interaction to virtual interaction was challenging for educators over the world during the Covid-19 pandemic outbreak. This paradigm shift in teaching and the use of webinars as the primary resource for teaching has shown a significant impact on the learning patterns of both educators and students. This paper presents a case study conducted on educators of Chitkara University, Rajpura Punjab, India. The goal is to Figure out their experience of using webinars as a teaching tool during the Covid-19 period for engineering undergraduates. It also discusses the challenges and issues faced by faculty members while conducting webinars. The usability score for webinars as a tool for teaching comes as 68.22%, which infers that a significant number of faculty members find it a useful tool for teaching their course. Further, several webinars platform explored by educators, out of which Go-To-Webinar platform is most favorable for the majority of faculty members. © 2020 IEEE.